Search: “models”

metasignal: A Python Package for Comprehensive Metacognitive Analysis and Decision-Making

arXiv:2607.29093v1 Announce Type: cross Abstract: Metasignal is an open-source Python package for signal detection theory (SDT) and metacognitive measurement. It implements the 17 metacognitive measur

safetyarxiv-cs-ai

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MHRGait: Gait Recognition from Momentum Human Rig Pose

DGX agent

arXiv:2607.29083v1 Announce Type: new Abstract: Gait recognition is shaped by its input representation. Silhouettes encode projected body shape, skeletons encode sparse joint coordinates, and 3D meshe

researcharxiv-cs-cv

3 Aug 2026

MOSAIC: Masked Outsourcing of Secure AI Computations

arXiv:2607.29221v1 Announce Type: cross Abstract: We address the challenge of securely and efficiently outsourcing AI computations from a trusted but computationally weak client to an untrusted but po

tutorialsarxiv-cs-ai

Multi-Source Multi-View Graph Domain Adaptation with Hyperbolic Residual Encoding for Cross-Site MDD Identification from rs-fMRI

arXiv:2607.29531v1 Announce Type: new Abstract: Cross-site identification of major depressive disorder (MDD) from resting-state functional magnetic resonance imaging (rs-fMRI) is hindered by inter-sit

safetyarxiv-cs-cv

Sakana Namazu: An LLM API with Japanese-vibes! 🎏 Built for Japanese enterprises, featuring frontier-level reasoning and built-in agentic to…

Sakana Namazu: An LLM API with Japanese-vibes! 🎏 Built for Japanese enterprises, featuring frontier-level reasoning and built-in agentic tools. 開発者の皆様、大変お待たせしました！Sakana Chatのモデルが遂にAPIとして公開です。ぜひお試しください

agentsdavid-ha--x

SATViz: Real-Time Visualization of Clausal Proofs

arXiv:2209.05838v2 Announce Type: replace Abstract: Visual layouts of graphs representing SAT instances can highlight the community structure of SAT instances. The community structure of SAT instances

Self-Play Meets Skill Evolution: Self-Evolving Search Agents that Pose, Solve, and Remember

arXiv:2607.29468v1 Announce Type: new Abstract: Self-play agents can generate training problems without questions from target benchmarks, but their curricula lack persistent state: failures affect gra

safetyarxiv-cs-ai

Technological Advances in Detecting and Managing Cognitive Impairment in Older Adults: Trends, Challenges, and Future Directions

arXiv:2607.28687v1 Announce Type: cross Abstract: As populations age, cognitive decline from mild cognitive impairment (MCI) to dementia is a defining health challenge of the coming decades, yet routi

TFGformer: Multivariate Time Series Forecasting via Time-Frequency Graph Learning and Covariate Fusion

arXiv:2607.29459v1 Announce Type: cross Abstract: Large-scale multivariate time series from heterogeneous IoT sensors demand accurate long-term forecasting for resource scheduling and predictive maint

The Checking Problem: What must be true before AI ships in a regulated firm

arXiv:2607.28666v1 Announce Type: new Abstract: Enterprise AI programmes stall at a rate that is widely quoted and poorly explained. This paper measures the mechanism. Six document-heavy workflows of

applicationsarxiv-cs-cl

Transcript-Managed Transformers: Monotone Multi-Agent Collapse and Universality with Two Pop-Enabled Transcripts

arXiv:2607.29496v1 Announce Type: new Abstract: We study transcript management for fixed, finite-precision causal Transformers. A transcript is partitioned into channels of bounded blocks. Each transi

agentsarxiv-cs-lg

What Makes a Sale? Simulating End-to-End Seller--Buyer Retail Dynamics with LLM Agents

arXiv:2604.04468v2 Announce Type: replace Abstract: Evaluating retail strategies before deployment is difficult, as outcomes are determined across multiple stages, from seller-side persuasion through

applicationsarxiv-cs-ai

When Unlearning Fails: Reliable Data Deletion under Post-Training in Agent Networks

arXiv:2607.28829v1 Announce Type: cross Abstract: Self-improving federated agent networks keep training after deployment by collecting new trajectories with the current policy and feeding them back in

safetyarxiv-cs-lg

It's not time to slow down but to accelerate! The recent AI-powered cyberattacks have everyone talking about the risks of AI. We should. But…

It's not time to slow down but to accelerate! The recent AI-powered cyberattacks have everyone talking about the risks of AI. We should. But let's not lose sight of the bigger picture! If we work hard

agentsclem-delangue--x

2 Aug 2026

To address the limits of deep learning and avoid stalling, the field of AI started by applying patch (1), which started being demoed 9 month…

researchfrancois-chollet--x

To address the limits of deep learning and avoid stalling, the field of AI started by applying patch (1), which started being demoed 9 months later in December 2024 and has now become completely ubiqu

2 Aug 2026

A Montage-Agnostic Encoder for Calibration-Light Cross-User Gesture Recognition from Surface Electromyography

arXiv:2607.27565v1 Announce Type: new Abstract: Pattern-recognition control promises a myoelectric prosthesis that responds to many intended gestures rather than one or two, but the promise has stayed

Audio-Anchored Fusion of Multi-Ratio DiT Reconstruction Residuals for Cross-Domain Audio Deepfake Detection

arXiv:2607.26472v1 Announce Type: cross Abstract: Audio deepfake detectors often degrade when generators, corpora, or recording conditions change. We use a Diffusion Transformer (DiT), trained only on

AWARE-FX: An Auditable Knowledge-Guided AI System for Measuring Corporate Foreign-Exchange Hedging Disclosure

arXiv:2607.27611v1 Announce Type: new Abstract: Corporate annual reports contain weakly structured evidence about foreign-exchange risk management, derivative use, natural hedging, and explicit non-us

researcharxiv-cs-cl

Balancing Privacy and Efficiency: Music Information Retrieval via Additive Homomorphic Encryption

arXiv:2508.07044v2 Announce Type: replace-cross Abstract: Modern music retrieval runs on vector embeddings, and once these embeddings are shared for search or matching they can be copied, probed, or u

Beyond Visual Ambiguity: Guiding Robust Monocular Depth Estimation in Challenging Scenarios via Detailed Long Captions

arXiv:2607.28285v1 Announce Type: new Abstract: Monocular depth estimation (MDE) faces challenges with non-Lambertian surfaces and adverse weather conditions due to the visual ambiguities inherent in

researcharxiv-cs-cv

Causal Discovery with Inverted Self-attention for Multivariate Time Series

arXiv:2607.28212v1 Announce Type: new Abstract: Causal discovery in multivariate time series data is challenging due to complex interactions, high dimensionality, and nonlinear dependencies among vari

researcharxiv-cs-cl

Local Ai

Cloud CISO Perspectives: Why AI Threat Defense is the new boardroom baseline

Welcome to the second Cloud CISO Perspectives for July 2026. Today, Chris Betz, CISO, Google Cloud, and Alicja Cade, Senior Director, Office of the CISO, Google Cloud, explain what boards of directors

local-aigoogle-cloud-ai

Context-Informed Ship Trajectory Prediction via Conditional Attention

arXiv:2607.27418v1 Announce Type: new Abstract: Long-term ship trajectory prediction is a fundamental capability for maritime safety and autonomous navigation. While recent Transformer-based architect

safetyarxiv-cs-lg

Decision-oriented joint optimization of evidence fusion based on event-conditioned credibility

arXiv:2504.04128v3 Announce Type: replace Abstract: In decision-level fusion tasks involving heterogeneous sources with unequal precision and potential anomalies, evidence deviating from the majority

Direct Rotor Thrust Sensing and Feedback Control for Disturbance Rejection of Multirotors Using Load-cells

arXiv:2607.10099v2 Announce Type: replace Abstract: Gust disturbances, dynamic vertical inflow and ground effect are key adverse aerodynamic phenomena that induce variations in the forces acting on a

researcharxiv-cs-ro

Distributions In, Distributions Out: The Case for Soft-Label Training

arXiv:2511.14117v2 Announce Type: replace Abstract: Supervised classifiers output a distribution over classes but are typically trained against a single label obtained by collapsing multiple annotator

Drawing-Recode: Annotation Grounding for Parametric CAD Code Generation from Raster 2D CAD Drawings

arXiv:2607.27558v1 Announce Type: new Abstract: Recovering Parametric CAD sequences from raster-format 2D Computer-Aided Design (CAD) drawings accumulated prior to digital transformation is important

applicationsarxiv-cs-cv

Driving up Inference Energy on SNNs: Per-Sample and Universal Sponge Attacks

arXiv:2607.27990v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) communicate through sparse binary spike events rather than dense activations, enabling energy-efficient inference on ne

Endo-NeRF++: Uncertainty-Aware Neural Rendering with Multi-Resolution Hash Encoding for Dynamic Surgical Scene Reconstruction

arXiv:2607.27825v1 Announce Type: cross Abstract: Reconstructing dynamic surgical scenes is crucial for robot-assisted minimally invasive surgery; however, it continues to be difficult because of tiss

researcharxiv-cs-cv

Event-Structured Physics-Informed Neural Networks for Differentiable Critical Clearing Boundaries

arXiv:2607.27681v1 Announce Type: new Abstract: Transient-stability assessment determines whether a power system can recover after a disturbance and is therefore essential to preventing generator trip

Local Ai

Explaining Image Similarity with Automatically Extracted Concept Activation Vectors

arXiv:2607.28386v1 Announce Type: new Abstract: Image similarity underlies many computer vision applications, yet it is often unclear why two images receive a high or low similarity score. Existing ex

local-aiarxiv-cs-cv

FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning

arXiv:2607.27665v1 Announce Type: new Abstract: Federated graph learning enables collaborative training over decentralized graph data without sharing raw graph information. As such risks evolve, clien

tutorialsarxiv-cs-lg

Hardware

FinCacheServe: Dependency-Consistent Answer Reuse for Cost-Efficient RAG Serving over Mutable Enterprise Documents

arXiv:2607.26076v1 Announce Type: cross Abstract: Retrieval-augmented generation services over mutable enterprise documents repeatedly execute semantically equivalent analysis requests. Answer reuse c

hardwarearxiv-cs-ai

Fully Inductive Cardinality Estimation

arXiv:2607.28311v1 Announce Type: cross Abstract: Query optimization of Basic Graph Patterns (BGP) SPARQL queries over Knowledge Graphs (KG) requires accurate cardinality estimation. Recently publishe

applicationsarxiv-cs-lg

Generalization Bounds on Optimal Control for Transformer Training and Wasserstein Distributional Robustness

arXiv:2607.27975v1 Announce Type: new Abstract: We derive finite-sample generalization bounds for Transformers trained with dynamic programming recursions. Building on the doubly lifted, measure-value

Graph Is the Verifier: Agentic Reinforcement Learning for Interprocedural Vulnerability Detection

arXiv:2607.26656v1 Announce Type: cross Abstract: Real-world vulnerabilities often span multiple functions, yet most learning-based detectors classify each function in isolation: on a sample of real C

safetyarxiv-cs-ai

Heterogeneous Ranking in Industrial-Scale Recommender Systems: A Case Study

arXiv:2607.27577v1 Announce Type: cross Abstract: Heterogeneous recommendation feeds present complex challenges that extend beyond those found in highly homogeneous environments (e.g., music-only or v

safetyarxiv-cs-lg

Improved Classification of Nitrogen Stress Severity in Plants Under Combined Stress Conditions Using Spatio-Temporal Deep Learning Framework

arXiv:2509.06625v3 Announce Type: replace Abstract: Plants in their natural habitats endure an array of interacting stresses, both biotic and abiotic, that rarely occur in isolation. Nutrient stress-p

researcharxiv-cs-cv

Improving the Robustness/Accuracy Tradeoff Against Adversarial Attacks Using Information Bottleneck Distillation Through Dual Teachers

arXiv:2607.27737v1 Announce Type: new Abstract: Deep neural networks (DNNs) have achieved remarkable success in classical machine learning problems. However, they are known to be vulnerable to adversa

Information Bottleneck Learning for Faithful Time Series Forecasting Explanations

arXiv:2607.28124v1 Announce Type: new Abstract: As forecasts increasingly drive decisions in fields such as energy, transportation, and healthcare, understanding the historical data behind these predi

applicationsarxiv-cs-lg

Isolating to Harness: Cross-Division Distillation for Fully Unsupervised Anomaly Detection

arXiv:2508.18007v2 Announce Type: replace Abstract: Fully Unsupervised Anomaly Detection (FUAD) addresses the practical scenario where training data is contaminated with unlabeled anomalies. This sett

tutorialsarxiv-cs-cv

Learning to Detect Cyber Attacks: Neural Anomaly Detection for Cybersecurity with Theoretical Insights

arXiv:2409.08521v2 Announce Type: replace-cross Abstract: In cybersecurity practice, new forms of cyberattacks continuously emerge, deliberately designed to evade defense systems that rely on previous

tutorialsarxiv-cs-lg

MIND: Multimodal Intent-Driven Network via Diffusion Transformers for Medical Image Fusion

arXiv:2607.28565v1 Announce Type: new Abstract: Medical image fusion aims to integrate complementary information from diverse imaging modalities to support clinical diagnosis. Existing methods typical

safetyarxiv-cs-cv

MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian

arXiv:2209.00546v5 Announce Type: replace-cross Abstract: Signed and directed networks are ubiquitous in real-world applications. However, there has been relatively little work proposing spectral grap

applicationsarxiv-cs-lg

Local Ai

Now You Have My Healthy Attention: A U-DiT for Brain-MRI Inpainting

arXiv:2607.27974v1 Announce Type: new Abstract: The ASNR-MICCAI BraTS Local Synthesis (Inpainting) task asks for the anatomically plausible completion of healthy brain tissue within a masked region of

local-aiarxiv-cs-cv

Pramana: A Composable, Domain-Specific Backend for Empirical Networking Research

arXiv:2607.26352v1 Announce Type: cross Abstract: Networking research advances by turning hypotheses into empirical evidence, so accelerating it means reducing the lag between ideation (synthesizing a

agentsarxiv-cs-ai

Quadratic Objective Perturbation: Curvature-Based Differential Privacy

arXiv:2605.05905v2 Announce Type: replace Abstract: Objective perturbation is a standard mechanism in differentially private empirical risk minimization. In particular, Linear Objective Perturbation (

Random Projection Flows for Efficient Manifold Density Estimation

arXiv:2509.25228v3 Announce Type: replace Abstract: Accurate density estimation is crucial for understanding complex high-dimensional data, but it becomes challenging when the data lies on or near low